Nonlinear modeling of FES-supported standing-up in paraplegia for selection of feedback sensors

Kamnik, R., Shi, J.Q., Murray-Smith, R. and Bajd, T. (2005) Nonlinear modeling of FES-supported standing-up in paraplegia for selection of feedback sensors. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(1), pp. 40-52. (doi:10.1109/TNSRE.2004.841879)

[img]
Preview
Text
nonlinear.roderick.pdf

1MB

Publisher's URL: http://dx.doi.org/10.1109/TNSRE.2004.841879

Abstract

This paper presents analysis of the standing-up manoeuvre in paraplegia considering the body supportive forces as a potential feedback source in functional electrical stimulation (FES)-assisted standing-up. The analysis investigates the significance of arm, feet, and seat reaction signals to the human body center-of-mass (COM) trajectory reconstruction. The standing-up behavior of eight paraplegic subjects was analyzed, measuring the motion kinematics and reaction forces to provide the data for modeling. Two nonlinear empirical modeling methods are implemented-Gaussian process (GP) priors and multilayer perceptron artificial neural networks (ANN)-and their performance in vertical and horizontal COM component reconstruction is compared. As the input, ten sensory configurations that incorporated different number of sensors were evaluated trading off the modeling performance for variables chosen and ease-of-use in everyday application. For the purpose of evaluation, the root-mean-square difference was calculated between the model output and the kinematics-based COM trajectory. Results show that the force feedback in COM assessment in FES assisted standing-up is comparable alternative to the kinematics measurement systems. It was demonstrated that the GP provided better modeling performance, at higher computational cost. Moreover, on the basis of averaged results, the use of a sensory system incorporating a six-dimensional handle force sensor and an instrumented foot insole is recommended. The configuration is practical for realization and with the GP model achieves an average accuracy of COM estimation 16 /spl plusmn/ 1.8 mm in horizontal and 39 /spl plusmn/ 3.7 mm in vertical direction. Some other configurations analyzed in the study exhibit better modeling accuracy, but are less practical for everyday usage.

Item Type:Articles
Keywords:Artificial neural network (ANN), Gaussian process (GP), center of mass (COM), feedback, functional electrical stimulation (FES), mixture models, paraplegia, position tracking, standing-up
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick
Authors: Kamnik, R., Shi, J.Q., Murray-Smith, R., and Bajd, T.
Subjects:R Medicine > RM Therapeutics. Pharmacology
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publisher:Institute of Electrical and Electronics Engineers (IEEE)
ISSN:1534-4320
Copyright Holders:Copyright © 2005 Institute of Electrical and Electronics Engineers (IEEE)
First Published:First published in IEEE Transactions on neural systems and rehabilitation engineering 13(1):40-52
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

University Staff: Request a correction | Enlighten Editors: Update this record